1. Field of the Invention
The present invention relates to an adaptive artificial vision method and to an adaptive artificial vision system.
2. Technical Background
In most artificial vision systems, objects are either pregiven or they do not exist.
Some systems perform image segmentation based on the particular characteristics of an image (color, boundaries, etc.). These systems have no notion of objects. They just extract regions which seem interesting in themselves. They work well if the background on which the “objects” are presented is known or strongly constrained (e.g. colored objects on a white floor). In such cases the segments automatically extracted can be considered as the “contour” of some objects.
Other systems perform object identification given a set of predefined objects that they use as models. If the models are of sufficiently good quality, performances of such systems can be very good. See for example the handbook from S. Ullman entitled “High-level vision: object recognition and visual cognition”, MIT Press, Boston, Mass., USA, 1996.
Unfortunately in some situations, neither of these two conditions can be met. This is particularly true, in the case of robots evolving in natural unknown environment, trying to discover the “objects” present without knowing them in advance. In such cases, segmenting and recognizing objects become a bootstrapping problem that can be summarized in the following way:                Segmentation algorithms do not work well in real-life conditions if no template of the objects is provided.        Templates of the objects cannot be built without a good segmentation algorithm.        
This situation leads to a technological deadlock.